Practical classification problems often possess skewed label distributions,
which pose a challenge for standard learners.
This problem of learning under class imbalance [1]}, [2]}, [3]},
or long-tail learning,
has received renewed interest in the context of neural models [4]}, [5]}, [6]}.
Successful approaches to the problem
include modifying the training data (e.g., by up- or down-sampling different labels [7]}, [2]}, [9]}, [10]}, [11]}, [12]}, [13]}),
modifying the classification rule (e.g., by applying varying thresholds for the different classes [14]}, [15]}, [16]}, [17]}, [18]}),
and
modifying the loss function (e.g., by penalising errors on rare labels more strongly [19]}, [20]}, [21]}, [22]}, [23]}, [24]}, [25]}, [26]}, [27]}, [28]}, [29]}).
